---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: swin-base-patch4-window7-224-finetuned-ind-17-imbalanced-aadhaarmask
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.855683269476373
    - name: Recall
      type: recall
      value: 0.855683269476373
    - name: F1
      type: f1
      value: 0.8542203503644927
    - name: Precision
      type: precision
      value: 0.8559779206156822
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# swin-base-patch4-window7-224-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3209
- Accuracy: 0.8557
- Recall: 0.8557
- F1: 0.8542
- Precision: 0.8560

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5155        | 0.9974 | 293  | 0.5710          | 0.7935   | 0.7935 | 0.7821 | 0.7895    |
| 0.4245        | 1.9983 | 587  | 0.4729          | 0.8238   | 0.8238 | 0.8187 | 0.8266    |
| 0.4183        | 2.9991 | 881  | 0.4145          | 0.8408   | 0.8408 | 0.8309 | 0.8350    |
| 0.4088        | 4.0    | 1175 | 0.3901          | 0.8425   | 0.8425 | 0.8375 | 0.8501    |
| 0.3489        | 4.9974 | 1468 | 0.3703          | 0.8463   | 0.8463 | 0.8446 | 0.8518    |
| 0.3115        | 5.9983 | 1762 | 0.3500          | 0.8540   | 0.8540 | 0.8525 | 0.8605    |
| 0.3087        | 6.9991 | 2056 | 0.3338          | 0.8519   | 0.8519 | 0.8494 | 0.8582    |
| 0.2372        | 8.0    | 2350 | 0.3181          | 0.8548   | 0.8548 | 0.8543 | 0.8587    |
| 0.2816        | 8.9974 | 2643 | 0.3167          | 0.8536   | 0.8536 | 0.8530 | 0.8561    |
| 0.2378        | 9.9745 | 2930 | 0.3063          | 0.8702   | 0.8702 | 0.8686 | 0.8709    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.19.0
- Tokenizers 0.19.1